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Preprint ARTICLE | doi:10.20944/preprints201609.0103.v1

Predicting the Outcome of NBA Playoffs Based on Maximum Entropy Principle

Ge Cheng, Zhenyu Zhang, Moses Ntanda Kyebambe, Nasser Kimbugwe

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: Maximum entropy model; K-means clustering; accuracy; classification; sports forecasting

Online: 27 September 2016 (11:10:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201607.0001.v1

Forecasting Wholesale Electricity Prices With Artificial Intelligence Models: The Italian Case

Murad Harasheh

Subject: Business, Economics And Management, Finance Keywords: PUN, artificial intelligence models, regression tree, bootstrap aggregation, forecasting error

Online: 2 July 2016 (03:48:36 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0260.v1

Multivariate Hydrologycal Modelling based on Long Short Term Memory Networks for Water Level Forecasting.

Jackson B. Renteria - Mena, Douglas Plaza, Eduardo Giraldo

Subject: Engineering, Electrical And Electronic Engineering Keywords: Multivariate hydrological modeling, Time series analysis, Water level forecasting, ; LSTM.

Online: 3 April 2024 (11:08:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202402.1547.v1

Time Series Forecasting with Missing Values Using Multiple Models: An Application to Wind Energy Systems

Xiaoou Li

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: time series forecasting; ARIMA; neural networks; transfer learning; wind energy systems

Online: 28 February 2024 (11:27:50 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0977.v1

Combined K-means Clustering with Neural Networks Methods for PV Short-Term Generation Load Forecasting in Electric Utilities

Alex Sleiman, Wencong Su

Subject: Engineering, Electrical And Electronic Engineering Keywords: machine learning; neural networks; PV power forecasting; smart meters; solar energy

Online: 15 November 2023 (09:52:40 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0438.v1

Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence

Amélia Ferreira da Silva, José Henrique Brito, Mariline Lourenço, José Manuel Pereira

Subject: Business, Economics And Management, Accounting And Taxation Keywords: Artificial Intelligence; Forecasting; Business Failure; Financial Sustainability; Financial Indicators; Transport Sector

Online: 7 November 2023 (11:19:43 CET)

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Preprint ARTICLE | doi:10.20944/preprints202309.0354.v1

The Use of a Digital Brix Refractometer to Estimate Cheese-Making Constituents in Ewes’ Milk

Massimo Todaro, Riccardo Gannuscio, Isabella Mancuso, Barbara Ducato, Maria Luisa Scatassa

Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: sheep milk constituents; Brix refractometer; milk protein percentages; Refractometer forecasting ability.

Online: 6 September 2023 (10:12:17 CEST)

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Preprint REVIEW | doi:10.20944/preprints202308.1578.v1

Trends on Wind Speed Forecasting: Umbrella Review of the Last 5 Years

Paulo Alexandre Costa Rocha, Carla Freitas De Andrade, André Valente Bueno, Fernanda Leite Lobo, Mona Lisa Moura de Oliveira, Jesse Van Griensven Thé, Bahram Gharabaghi

Subject: Engineering, Mechanical Engineering Keywords: forecasting; prevision; wind speed; wind power; renewable energy; Scopus base; Bibliometrix

Online: 23 August 2023 (07:27:24 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0934.v1

Algorithm Optimizer in GA-LSTM for Stock Price Forecasting

Yohanes Leonardus Sukestiyarno, Dian Tri Wiyanti, Lathifatul Azizah

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Time Series; Forecasting, Deep Learning; Genetic Algorithm; Long Short-Term Memory

Online: 12 May 2023 (11:07:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202108.0268.v1

An Auto-Weighting Aggregative Fuzzy Collaborative Intelligence Approach for DRAM Yield Forecasting

Hsin-Chieh Wu, Tin-Chih Toly Chen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Fuzzy collaborative intelligence; Dynamic random access memory; Fuzzy weighted intersection; Forecasting

Online: 11 August 2021 (18:08:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202104.0138.v1

Comparison of Forecasting Ability for Energy Consumption in BRICS: ARIMA (1,1,1) and FGM (1, 1) Models

Atif Maqbool Khan, Magdalena Osinska

Subject: Business, Economics And Management, Accounting And Taxation Keywords: Energy consumption; BRICS; GM (1, 1); Fractional-order; GREY; Forecasting accuracy

Online: 5 April 2021 (13:51:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202012.0619.v1

An Adaptive and Resilient Islanding Approach for Power System Considering Secondary Collapses

Sanaullah Kaka, Mingchao Xia, Mazhar Hussain, Ali Raza Meer, Shubash Kumar

Subject: Engineering, Automotive Engineering Keywords: Adaptive Island; Minimum spanning tree; Load forecasting; Controllable switches; Distributed generations

Online: 24 December 2020 (12:35:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints202010.0565.v1

Modeling and Forecasting of Lake Malombe Fish Biomass and Catch Per Unit Effort (CPUE)

Rodgers Makwinja, Seyoum Mengistou, Emmanuel Kaunda, Tena Alemiew, Titus Phiri, Ishmael Kosamu, Chikumbusko Kaonga

Subject: Biology And Life Sciences, Anatomy And Physiology Keywords: ARIMA, CPUE, Fish biomass landings, Forecasting, Lake Malombe, Time series approach

Online: 27 October 2020 (21:04:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints201811.0490.v1

Forecasting Performances of the Reduced Form VAR and Sims-Zha Bayesian VAR Models when the Multiple Time Series are Jointly Influenced by Collinearity and Autocorrelated Error

Monday Osagie Adenomon, Benjamin Agboola Oyejola

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: forecasting; time series; vector autoregression (VAR), bayesian VAR; collinearity and autocorrelation

Online: 20 November 2018 (09:03:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints201807.0440.v2

Ownership Cost Comparison of Battery Electric and Non-Plugin Hybrid Vehicles: A Consumer Perspective

Lawrence Fulton

Subject: Engineering, Automotive Engineering Keywords: BEV; ownership cost analysis; design of experiments; forecasting; Monte Carlo simulation

Online: 17 August 2018 (12:59:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201609.0104.v1

Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

Tiannan Ma, Dongxiao Niu

Subject: Computer Science And Mathematics, Computer Science Keywords: icing forecasting; fireworks algorithm; least square support vector machine; feature selection

Online: 27 September 2016 (11:16:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0209.v1

A Single Buyer Model of Imbalance Cost Pass-Through Pricing Forecasting under Malaysia Electricity Supply Industry

Fatin Khairunnisa Khairuddin, Farah Anishah Zaini, Mohamad Fani Sulaima, Nur Hazahsha Shamsudin, Mohd Shahrin Abu Hanifah

Subject: Engineering, Electrical And Electronic Engineering Keywords: Electricity price; Incentive Based Regulation; Electricity Market; Forecasting; Time Series; Machine Learning

Online: 2 April 2024 (12:34:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202311.1712.v1

Graph-based Neural Networks Framework using Microcontrollers for Energy-Efficient Traffic Forecasting

Sorin Zoican, Roxana Zoican, Dan Galatchi, Marius-Constantin C Vochin

Subject: Engineering, Electrical And Electronic Engineering Keywords: graph-based neural network, traffic forecasting, Internet of Things, Contiki operating system

Online: 27 November 2023 (15:00:05 CET)

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Preprint ARTICLE | doi:10.20944/preprints202309.1966.v1

Hybrid Model of VMD and LSTM for Next-Hour Wind Speed Forecasting in a Hot Desert Climate

Ghadah Alkhayat, Syed Hamid Hasan, Rashid Mehmood

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: wind speed forecasting; deep learning; LSTM; GRU; wind energy; CEEMDAN; EMD; VMD

Online: 28 September 2023 (11:41:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.1637.v1

Constraining Flood Forecasting Uncertainties through Streamflow Data Assimilation in the Tropical Andes of Peru: Case of the Vilcanota River Basin

Harold Llauca, Miguel Arestegui, Waldo Lavado-Casimiro

Subject: Environmental And Earth Sciences, Water Science And Technology Keywords: Streamflow Data Assimilation; Flood forecasting; Tropical Andes; Satellite Precipitation Products; GR4H model

Online: 25 September 2023 (09:00:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.1400.v1

Global and Local Approaches for Forecasting of Long-Term Natural Gas Consumption in Poland Based on Hierarchical Short Time Series

Bartłomiej Gaweł, Andrzej Paliński

Subject: Business, Economics And Management, Econometrics And Statistics Keywords: hierarchical forecasting; short time series; local approach; global approach; household gas consumption

Online: 21 September 2023 (05:39:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202212.0534.v1

An IoT-based Bi-Cluster Forecasting using Automated ML-Model Optimization for COVID19

Hasan Tariq, Farid Touati, Damiano Crescini, Adel Ben Mnaouer

Subject: Environmental And Earth Sciences, Environmental Science Keywords: Indoor air quality; forecasting; machine learning; IoT; Covid-19; environmental mapping; pandemic.

Online: 28 December 2022 (09:10:46 CET)

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Preprint ARTICLE | doi:10.20944/preprints202004.0257.v1

Predictive Analytics of COVID-19 Using Information, Communication and Technologies

Parikshit N. Mahalle, Nilesh P. Sable, Namita P. Mahalle, Gitanjali R. Shinde

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: COVID-19; predictive analytics; forecasting models; Machine Learning Method; prediction; epidemic; pandemic

Online: 16 April 2020 (05:54:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201609.0053.v1

Applying Multi-Output Random Forest Models to Electricity Price Forecast

Camino González, José M. Mira, José A. Ojeda

Subject: Engineering, Control And Systems Engineering Keywords: electricity markets; price forecasting; multi-output models; random forests; conditional inference trees

Online: 18 September 2016 (06:16:19 CEST)

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Preprint CASE REPORT | doi:10.20944/preprints202309.1851.v1

Oxygen demand forecasting and optimal scheduling of the oxygen gas system in the iron- and steel-making enterprises

Peikun Zhang, Li Wang

Subject: Engineering, Control And Systems Engineering Keywords: Iron- and steel-making enterprise; Oxygen system; Forecasting model; Scheduling model; Energy-saving

Online: 28 September 2023 (04:21:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202110.0302.v1

Application of ALO-ELM in Load Forecasting Based on Big Data

Ming He, Yi Li, Wan Zou, Xiangxi Duan

Subject: Engineering, Electrical And Electronic Engineering Keywords: load forecasting; extreme learning machine (ELM); ant lion optimization (ALO) ；parameter optimization; model.

Online: 21 October 2021 (09:34:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202104.0398.v1

A Two-Stage Polynomial and Nonlinear Growth Approach for Modeling the COVID-19 Outbreak in Mexico

Rafael Perez Abreu, Samantha Estrada, Héctor de-la-Torre-Gutiérrez

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: COVID-19; epidemic modeling; time series forecasting; nonlinear growth models; Prais-Winsten estimation

Online: 15 April 2021 (07:44:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0404.v1

Recalibration and Recommendation for the Prediction of the California Bearing Ratio (CBR) from Dynamic Cone Penetrometer (DCP)

Jirawat Chokkerd, Artit Udomchai, Sivarit Sultornsanee, Nopanom Kaewhanam

Subject: Engineering, Civil Engineering Keywords: California Bearing Ratio; CBR; Dynamic Cone Penetrometer; DCP; Estimation, Prediction; Forecasting; Subgrade; Sand; Soil

Online: 5 April 2024 (15:56:17 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202310.0310.v3

Pivot Clustering to Minimize Error in Forecasting Aggregated ARMA Demand

Vladimir Kovtun, Avi Giloni, Clifford Hurvich, Sridhar Seshadri

Subject: Business, Economics And Management, Econometrics And Statistics Keywords: forecasting aggregate demand; clustering time series; pivot clustering; ARMA model; order-up-to policy

Online: 25 October 2023 (13:20:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202309.0717.v1

Connection between Variations of the Probability Distribution of the Recurrence Time and Phases of the Seismic Activity

Elisa Varini, Renata Rotondi

Subject: Environmental And Earth Sciences, Environmental Science Keywords: Interevent time; Probability distributions; probabilistic forecasting; seismic cycle; statistical seismology; statistical methods; Bayesian inference

Online: 12 September 2023 (10:13:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.1581.v1

Extension of Fuzzy ELECTRE I for Evaluating Demand Forecasting Methods in Sustainable Manufacturing

Ta-Chung Chu, Thi Bich Ha Nghiem

Subject: Business, Economics And Management, Other Keywords: Demand forecasting methods; Fuzzy ELECTRE I; Extended modified discordance matrix; Hadamard product; Closeness coefficient

Online: 22 August 2023 (12:44:02 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.0292.v1

A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers

Sebastian C Ibañez, Christopher Pineda Monterola

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: crop production; agricultural production; time series forecasting; artificial intelligence; transformer; machine learning; deep learning

Online: 3 August 2023 (10:34:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0111.v1

Short-Term Load Forecasting of the Greek Power System Using a Dynamic Block-Diagonal Fuzzy Neural Network

George Kandilogiannakis, Paris Mastorocostas, Athanasios Voulodimos, Constantinos Hilas

Subject: Engineering, Electrical And Electronic Engineering Keywords: Greek power system; electric load forecasting; block-diagonal neurons; fuzzy neural network; internal feedback

Online: 3 May 2023 (07:34:20 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202206.0086.v1

Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting

Pablo Boullosa, Adrián Garea, Iván Carlos Area-Carracedo, Juan Nieto, Jorge Mira

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: non-parametric modeling; flu; influenza; COVID-19; SARS-CoV-2; Empirical Dynamic Modeling; forecasting

Online: 6 June 2022 (10:24:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202103.0310.v1

Unfolding Jellyfish Bloom Dynamics Along the Mediterranean Basin by Transnational Citizen Science Initiatives

Macarena Marambio, Antonio Canepa, Laura Lopez, Adam Gauci, Sonia Gueroun, Serena Zampardi, Ferdinando Boero, Ons Kefi-Daly Yahia, Mohamed Nejib Daly Yahia, Veronica Fuentes, Stefano Piraino, Alan Deidun

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Gelatinous zooplankton; Scyphozoa; Pelagia noctiluca; Rhizostoma pulmo; Forecasting system; Mitigation tool; Coastal zone management

Online: 11 March 2021 (10:57:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202011.0205.v1

Implementation of Water Demand Forecasting Model to Aid Sustainable Water Supply Chain Management in UAE

Vian Ahmed, Ahmad Saad, Hasan Saleh, Sara Saboor, Nikita Kasianov, Tahani Alnaqbi

Subject: Engineering, Control And Systems Engineering Keywords: Neural Networks; Long-Short Term Model; Water demand; Forecasting; Sustainable development goals; Water Goal.

Online: 5 November 2020 (10:17:30 CET)

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Preprint ARTICLE | doi:10.20944/preprints202007.0409.v1

Modeling and Detection of Future Cyber-Enabled DSM Data Attacks

Kostas Hatalis, Chengbo Zhao, Parv Venkitasubramaniam, Larry Snyder, Shalinee Kishore, Rick S. Blum

Subject: Engineering, Electrical And Electronic Engineering Keywords: Demand Side Management; Demand Response; Cyber-Physical Systems; Dynamic Pricing; Load Forecasting; Attack Detection

Online: 19 July 2020 (11:14:01 CEST)

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Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinear relationship between load management and real-time pricing, (ii) simulating residential load data and prices, (iii) creating cyber-attacks, and (iv) detecting said attacks. In this framework, we first develop time-series forecasts to model load demand and use them as inputs to an elasticity model for the price-demand relationship in the DSM loop. This work then investigates the behavior of such a feedback loop under intentional cyber-attacks. We simulate and examine load-price data under different DSM-participation levels with three types of random additive attacks: ramp, sudden, and point attacks. We conduct two investigations for the detection of DSM attacks. The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors. Results conclude that higher amounts of DSM participation can exacerbate ramp and sudden attacks leading to better detection of such attacks, especially with supervised learning classifiers. We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.

Preprint ARTICLE | doi:10.20944/preprints202004.0063.v1

A Proposal for Isotherm World Maps to Forecast the Seasonal Evolution of the SARS-CoV-2 Pandemic

Nicola Scafetta

Subject: Biology And Life Sciences, Virology Keywords: SARS-CoV-2; COVID-19; Pandemic geographical distribution; Epidemic forecasting; Weather conditions; Climatic zones.

Online: 6 April 2020 (14:11:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201909.0182.v1

Development of Korean Nomograms for Forecasting and Alerting Debris Flows Based on Critical Accumulated Rainfall

Dong-Ho Nam, Suk-Ho Lee, Byung-Sik Kim

Subject: Engineering, Civil Engineering Keywords: rainfall intensity; debris flow forecasting; rainfall triggering index (RTI); critical accumulated rainfall (Rc); nomogram

Online: 17 September 2019 (06:09:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201905.0361.v1

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband

Subject: Engineering, Civil Engineering Keywords: river flow forecasting; optimization; M5 model tree; support vector regression; fruit fly optimization algorithm

Online: 30 May 2019 (05:33:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201905.0320.v1

Support Vecztor Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband, Shatirah Akib

Subject: Engineering, Civil Engineering Keywords: river flow forecasting; optimization; M5 model tree; support vector regression; fruit fly optimization algorithm

Online: 27 May 2019 (13:01:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201807.0019.v1

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting

Benjamin Auder, Jairo Cugliari, Yannig Goude, Jean-Michel Poggi

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Clustering; Forecasting; Hierarchical Time-Series; Individual Electrical Consumers; Scalable; Short Term; Smart Meters; Wavelets

Online: 2 July 2018 (17:43:29 CEST)

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Preprint REVIEW | doi:10.20944/preprints202312.1864.v1

Decoding Financial Futures: The Power of Numerical Indicators in Predicting Bankruptcy

Dimitris Billios, Dimitra Seretidou, Antonios Stavropoulos

Subject: Business, Economics And Management, Finance Keywords: bankruptcy; early warning; failure prediction; financial distress; prediction; forecasting; numerical indicators; statistical analysis; PRISMA standards.

Online: 25 December 2023 (10:25:37 CET)

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Preprint ARTICLE | doi:10.20944/preprints202307.1567.v1

Energy Consumption Forecasting in Commercial Buildings during the Covid-19 Pandemic: A Multivariate Multilayered Long-Short Term Memory Time-series Model with Knowledge Injection

Tan Ngoc Dinh, Gokul Sidarth Thirunavukkarasu, Mehdi Seyedmahmoudian, Saad Mekhilef, Alex Stojcevski

Subject: Engineering, Electrical And Electronic Engineering Keywords: Energy consumption prediction; Energy management; Time series forecasting; Building energy consumption forecast; Covid-19 pandemic

Online: 24 July 2023 (08:47:09 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202204.0073.v1

Enriching Artificial Intelligence Explanations with Knowledge Fragments

Jože Rožanec, Elena Trajkova, Inna Novalija, Patrik Zajec, Klemen Kenda, Blaž Fortuna, Dunja Mladenić

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Explainable Artificial Intelligence; Human-Centric Artificial Intelligence; Smart Manufacturing; Demand Forecasting; Industry 4.0; Industry 5.0

Online: 8 April 2022 (08:11:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202108.0246.v1

Explaining Bad Forecasts in Global Time Series Models

Jože Rožanec, Elena Trajkova, Klemen Kenda, Blaž Fortuna, Dunja Mladenić

Subject: Engineering, Electrical And Electronic Engineering Keywords: Explainable Artificial Intelligence; XAI; Time Series Forecasting; Global Time Series Models; Machine Learning; Artificial Intelligence

Online: 11 August 2021 (10:32:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201801.0051.v1

Smart Building: Use of the ANN Approach for Indoor Temperature Forecasting

Nivine Attoue, Isam Shahrour, Rafic Younes

Subject: Engineering, Control And Systems Engineering Keywords: smart building; artificial neural network (ANN); indoor; temperature; façade; outdoor; forecasting; relevance; sensors; recorded data

Online: 8 January 2018 (08:58:09 CET)

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Preprint ARTICLE | doi:10.20944/preprints202404.1492.v1

Scalable and Interpretable Forecasting of Hydrological Time-Series based on Variational Gaussian Processes

Julián David Pastrana-Cortés, Julian Gil-Gonzalez, Andrés Marino Álvarez-Meza, David Augusto Cárdenas-Peña, Álvaro Angel Orozco-Gutiérrez

Subject: Electrical And Electronic Engineering, Engineering Keywords: Autoregressive models; Gaussian Process; Long Short Term Memory networks; Multi-task forecasting; Predictive Distribution; Streamflow Contributions

Online: 23 April 2024 (11:57:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202302.0383.v1

Modelling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion

Albertus Untadi, Lily D Li, Michael Li, Roland Dodd

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: Building fire; Socioeconomic determinants; South East Queensland; Predictive model; Forecasting; Backward elimination; Robust final predictor error

Online: 22 February 2023 (09:37:47 CET)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202209.0404.v1

Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

Munshi Md Shafwat Yazdan, Marzieh Khosravia, Shah Saki, Md Abdullah Al Mehedi

Subject: Engineering, Energy And Fuel Technology Keywords: Recurrent Neural Network; Renewable Energy; Power consumption; Open Power System Data; Multivariate Exploratory; Time series forecasting

Online: 27 September 2022 (02:44:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202204.0107.v1

On the Redox-Activity and Health-Effects of Atmospheric Primary and Secondary Aerosol: Phenomenology

Francesca Costabile, Stefano Decesari, Roberta Vecchi, Franco Lucarelli, Gabriele Curci, Dario Massabò, Matteo Rinaldi, Maurizio Gualtieri, Emanuela Corsini, Elena Menegola, Silvia Canepari, Lorenzo Massimi, Stefania Argentini, Maurizio Busetto, Gianluca Di Iulio, Luca Di Liberto, Marco Paglione, Igor Petenko, Mara Russo, Angela Marinoni, Gianpietro Casasanta, Sara Valentini, Vera Bernardoni, Federica Crova, Gianluigi Valli, Alice Corina Forello, Fabio Giardi, Silvia Nava, Giulia Pazzi, Paolo Prati, Virginia Vernocchi, Teresa La Torretta, Ettore Petralia, Milena Stracquadanio, Gabriele Zanini, Gloria Melzi, Emma Nozza, Martina Iulini, Donatella Caruso, Lucia Cioffi, Gabriele Imperato, Flavio Giavarini, Maria Battistoni, Francesca Di Renzo, Maria Agostina Frezzini, Cinzia Perrino, Maria Cristina Facchini

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: atmospheric aerosol; chemical composition; secondary aerosol; source apportionment; ultrafine particles; oxidative potential; exposure; toxicology; forecasting; micrometeorology

Online: 12 April 2022 (09:54:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201912.0102.v1

Predicting Environmental Contribution to Ship’s Average Fuel Consumption Based on Non-Uniform Time Set

Igor Vujović, Ivica Kuzmanić, Miro Petković, Joško Šoda

Subject: Engineering, Electrical And Electronic Engineering Keywords: forecasting of fuel demand; ship’s fuel consumption; data fitting; statistical quality measures; signal processing and analysis

Online: 8 December 2019 (17:05:03 CET)

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Preprint ARTICLE | doi:10.20944/preprints202308.0257.v1

LightGBM and SVM algorithms for Predicting Synthetic Load Profiles Using a Non-Intrusive Approach

Khumbulani Mpofu, Olukorede Tijani Adenuga, Olawale M Popoola, And Abel Mathebula

Subject: Engineering, Industrial And Manufacturing Engineering Keywords: Light Gradient Boosted; Machine learning; Non-Intrusive Approach; electric load forecasting; Support Vector Machine; Synthetic Load Profile.

Online: 3 August 2023 (10:37:48 CEST)

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Preprint REVIEW | doi:10.20944/preprints202304.1168.v1

Modes of Operation and Forcing in Oil Spill Modeling: State-of-Art, Deficiencies and Challenges

Panagiota Keramea, Nikolaos Kokkos, George Zodiatis, Georgios Sylaios

Subject: Environmental And Earth Sciences, Oceanography Keywords: oil spill modeling; meteorological and hydrodynamic forcing; wave models; met-ocean data; forecasting; biogeochemical models; oil biodegradation.

Online: 28 April 2023 (10:49:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202006.0353.v1

Analysis and Forecast of COVID-19 Pandemic in Pakistan

Abdul Bari Malik

Subject: Computer Science And Mathematics, Analysis Keywords: COVID-19; Epidemiology; COVID-19 Analysis and Forecast in Pakistan; Forecasting; Estimation; ARIMA; Prophet; SIRD; Diffusion; Analysis

Online: 29 June 2020 (10:50:47 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0397.v1

Forecasting COVID-19-Associated Hospitalizations under Different Levels of Social Distancing in Lombardy and Emilia-Romagna, Northern Italy: Results from an Extended SEIR Compartmental Model

Chiara Reno, Jacopo Lenzi, Antonio Navarra, Eleonora Barelli, Davide Gori, Alessandro Lanza, Riccardo Valentini, Biao Tang, Maria Pia Fantini

Subject: Medicine And Pharmacology, Epidemiology And Infectious Diseases Keywords: coronavirus; COVID-19; SARS-CoV-2; pandemic; public health intervention; lockdown; resurgence; forecasting; mathematical modelling; SEIR model

Online: 22 April 2020 (08:48:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.1645.v1

The results and developments of the radon monitoring network in seismic areas

Victorin Emilian TOADER, Constantin Ionescu, Iren-Adelina Moldovan, Alexandru Marmureanu, Nicoleta-Sanda Brisan, Iosif Lıngvay, Andrei Mihai

Subject: Environmental And Earth Sciences, Environmental Science Keywords: radon and CO2 monitoring; anomaly detection; multidisciplinary monitoring; precursor phe-nomena; air ionization monitoring; OEF (Operational Earthquake Forecasting).

Online: 23 May 2023 (10:56:15 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201812.0201.v1

Bayesian VAR Modeling and Forecasting of the Dynamic Interrelationship between Economic Growth and Revenue from Oil and Non-Oil Sectors in Nigeria

Monday Osagie Adenomon

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: Bayesian Vector Autoregression (BVAR), Modeling, Forecasting, Gross Domestic Product (GDP), Economic Growth, Revenue, Oil sector, Non-oil Sector

Online: 17 December 2018 (15:56:09 CET)

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Preprint ARTICLE | doi:10.20944/preprints201804.0286.v1

Electricity Price Forecasting Using Recurrent Neural Networks

Umut Ugurlu, Ilkay Oksuz, Oktay Tas

Subject: Business, Economics And Management, Finance Keywords: electricity price forecasting; deep learning; gated recurrent units; long short term memory; artificial intelligence, turkish day-ahead market

Online: 23 April 2018 (11:38:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.0390.v1

Time Series Electrical Motor Drives Forecasting based on Simulation Modeling and Bidirectional Long-Short Term Memory

Thi-Thu-Huong Le, Yustus Eko Oktian, Uk Jo, Howon Kim

Subject: Computer Science And Mathematics, Computer Science Keywords: Three-phase DTC induction motor; simulation modeling; deep learning; Bi-LSTM; signal processing; time series forecasting; frequency domain; FFT

Online: 4 August 2023 (07:27:23 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202105.0261.v1

Assessing Point Forecast Bias Across Multiple Time Series: Measures and Visual Tools

Andrey Davydenko, Paul Goodwin

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: forecasting; forecast evaluation; forecast bias; mean bias; median bias; MPE; AvgRel-metrics; AvgRelAME; AvgRelAMdE; RelAME; RelMdE; AvgRelME; AvgRelMdE; OPc

Online: 12 May 2021 (09:48:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202104.0332.v1

Performance Assessment for Short-Term Water Demand Forecasting Models at an End-Use Level in Korea

Kang Min Koo, Kuk Heon Han, Kyung Soo Jun, Gyumin Lee, Jung Sik Kim, Kyung Taek Yum

Subject: Engineering, Automotive Engineering Keywords: smart water grid; advanced metering infrastructure; short-term water demand forecasting; end-use level; on-site sodium hypochlorite generator

Online: 13 April 2021 (09:20:08 CEST)

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It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real time through an advanced metering infrastructure (AMI) sensor, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include the autoregressive integrated moving average, radial basis function-artificial neural network, quantitative multi-model predictor plus, and long short-term memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand by applying the data on the amount of water consumption by purpose and the pipe diameter of an end-use level of the SWG demonstration plant in each demand forecasting model. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from the AMI, and the performance of each model was assessed. The Smart Water Grid Research Group installed ultrasonic-wave-type AMI sensors in the block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the residual, root mean square error (RMSE), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), and Pearson correlation coefficient (PCC) as indices. The water demand forecast was slightly underestimated in models that employed the assessment results based on the RMSE and NRMSE. Furthermore, the forecasting accuracy was low for the NSE due to a large number of negative values; the correlation between the observed and forecasted values of the PCC was not high, and it was difficult to forecast the peak amount of water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.

Preprint ARTICLE | doi:10.20944/preprints202301.0510.v1

Generation of Non-Linear Technique Based 6 Hourly Wind Reanalysis Products Using SCATSAT-1 and Numerical Weather Prediction Model Outputs

Suchandra A Bhowmic, Manisha Gupta, Abhishek Chakraborty, Neeraj Agarwal, Rashmi Sharma, M M Ali

Subject: Environmental And Earth Sciences, Space And Planetary Science Keywords: Winds; SCATSAT-1; NCMRWF (National Center for Medium Range Weather Forecasting), CCMP (Cross Calibrated Mul-ti-Platform) and Particle filter

Online: 28 January 2023 (03:03:19 CET)

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Preprint ARTICLE | doi:10.20944/preprints201907.0016.v1

Life Cycle Modeling of Structural Defects via Computational Geometry and Time Series Forecasting

Sara Mohamadi, David Lattanzi

Subject: Engineering, Civil Engineering Keywords: Remote sensing; Photogrammetry; Life cycle modeling; Time series forecasting; Structural damage; Stochastic modeling; Convex Hull; ARIMA; VAR; Fatigue crack prediction

Online: 1 July 2019 (12:23:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201808.0507.v1

Hydrologic Forecasts Verification and Comparison of Forecasting Methods

Sergei Borsch, Andrei Khristoforov, Vladimir Krovotynzev, Ekaterina Leontieva, Yuri Simonov, Victoria Deriugina

Subject: Environmental And Earth Sciences, Environmental Science Keywords: hydrologic forecast verification, mean squared forecast error, methods of forecast error estimation, comparison of hydrologic forecasting methods, forecast applicability assessment.

Online: 29 August 2018 (16:11:24 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202311.1538.v1

Short-Term Solar Irradiance Prediction with a Hybrid Ensemble Model using EUMETSAT Satellite Images

Jayesh Thaker, Robert Höller, Mufaddal Kapasi

Subject: Environmental And Earth Sciences, Sustainable Science And Technology Keywords: PV power forecasting; deterministic forecast; machine learning; deep learning; satellite forecast, ensemble models; solar; clear sky index, short-term forecast, NWP

Online: 23 November 2023 (13:39:39 CET)

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Accurate short-term solar irradiance forecasting is crucial for the efficient operation of solar energy driven photovoltaic (PV) power plants. In this research, we introduce a novel hybrid ensemble forecasting model that amalgamates the strengths of machine learning tree-based models and deep learning neuron-based models. The hybrid ensemble model integrates the interpretability of tree-based models with the capacity of neuron-based models to capture complex temporal dependencies within solar irradiance data. Furthermore, stacking and voting ensemble strategies are employed to harness the collective strengths of these models, significantly enhancing prediction accuracy. This integrated methodology is enhanced by incorporating pixels from satellite images provided by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). These pixels are converted into structured data arrays and employed as exogenous inputs in the algorithm. The primary objective of this study is to improve the accuracy of short-term solar irradiance predictions, spanning a forecast horizon up to 6 hours ahead. The incorporation of EUMETSAT satellite image pixels data enables the model to extract valuable spatial and temporal information, thus enhancing the overall forecasting precision. This research also includes detailed analysis of the derivation of GHI using satellite images. The study was carried out and the models tested across three distinct locations in Austria. A detailed comparative analysis was carried out for traditional Satellite (SAT) and Numerical Weather Prediction (NWP) models with hybrid models. Our findings demonstrate a higher skill score for all of the approaches compared to smart persistent model and consistently highlight the superiority of the hybrid ensemble model for short-term prediction window of 1 to 6 hours. This research underscores the potential for enhanced accuracy of the hybrid approach to advance short-term solar irradiance forecasting, emphasizing its effectiveness at understanding the intricate interplay of the meteorological variables affecting solar energy generation worldwide. The results of this investigation carry noteworthy implications for advancing solar energy systems, thereby supporting the sustainable integration of renewable energy sources into the electrical grid.

Preprint ARTICLE | doi:10.20944/preprints202304.0282.v2

Coastal Wave Modeling and Forecasting with LSTM Optimization For Sustainable Energy Harvesting

Zaharaddeeen Karami Lawal, Hayati Yassin, Daphne Teck Ching Lai, Azam Che Idris

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Numerical methods; Hyperparameters optimization; Ocean energy; Sustainable Energy; Recur-rent neural network (RNN); Time series forecasting; Sustainability and environmental man-agement

Online: 18 May 2023 (07:09:21 CEST)

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Coastal wave modeling and forecasting are essential in oceanography, sustainable marine energy, and ocean engineering. Precise forecasting of wave speed and direction are crucial for offshore operations, marine energy, risk management, environmental management, coastal and sustainable maritime management. The coastline of Brunei Darussalam can generate between 15 and 126 Giga Watt of wave energy. In this experimental research, we used two numerical approaches, the finite difference, and spectral element methods, to model and simulate wave speed and direction. We calculated the mean error between numerical and analytical solutions. We proposed a novel, promising, univariate time series forecasting model by combining the Long Short-Term (LSTM) with KerasTuner hyperparameters tuning and optimization techniques. This method helps us to improve the accuracy and efficiency of time-series forecasting.. The experimental data was computed from high-precision Acoustic Doppler Current Profiler (ADCP) sensor data. This research is part of the preliminary feasibility analysis of wave energy production in Brunei Darussalam and net zero commitment for a sustainable environment. Seven independent forecast experiments were performed for wave speed and direction in degree and radian units for 1, 3, 6, 8, 10, 12, and 24 hours. Mean squared error (MSE) was adopted as a metric for both training and testing. The experimental results reveal that the wave speed forecast has the lowest MSE compared to direction, regardless of the unit of measure, but has a longer duration. In addition, the direction forecast in the degree unit has the lowest errors compared to the unit of radians; the latter has a longer running time than the former. The model has delivered optimal results throughout the experiments with minor training and test errors. We conducted a thorough evaluations on two benchmark time series datasets, which include the study dataset and air quality index dataset, to validate the performance of the proposed model with other models. The proposed model outperforms cutting-edge forecasting models, such as the conventional LSTM, ARIMA, and Prophet. The model has the slightest forecast error compared to the existing literature’s result.

Preprint ARTICLE | doi:10.20944/preprints202201.0317.v1

Prevalence and Economic Costs of Absenteeism in an Aging Population – A Quasi-Stochastic Projection for Germany

Patrizio Vanella, Christina Benita Wilke, Doris Söhnlein

Subject: Business, Economics And Management, Econometrics And Statistics Keywords: Cohort-Component Method; Multivariate Methods; Time Series Analysis; Monte Carlo Methods; Stochastic Forecasting; Demography; Statistical Epidemiology; Labor Market Research; Health Economics

Online: 21 January 2022 (10:32:54 CET)

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Preprint ARTICLE | doi:10.20944/preprints202308.1230.v1

Explainable Approaches for Forecasting Building Electricity Consumption

Nikos D. Sakkas, Sofia Yfanti, Pooja Shah, Nikitas Sakkas, Christina Chaniotakis, Costas Daskalakis, Eduard Barbu, Marharyta Domnich

Subject: Engineering, Energy And Fuel Technology Keywords: electricity demand forecasting; model explainability; SHAP values; neural networks; structured time series; genetic programming (GP); symbolic expressions; training timeframe; counterfactuals; actionable features

Online: 17 August 2023 (09:06:07 CEST)

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Working Paper ARTICLE

APPLICATION OF NEURAL NETWORK TECHNOLOGIES IN PREDICTION OF COVID-19 INFECTION IN THE WORLD

Eduard Dadyan

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: time series; forecasting; neural networks; data preprocessing; training and control samples; coronavirus pandemics; Deductor Studio; data cleaning; partial processing; spectral processing; autocorrelation; sliding windows.

Online: 30 March 2021 (14:16:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202302.0315.v1

Characterising Hospital Admission Patterns and Length of Stay in the Emergency Department at Mater Dei Hospital Malta

Lalit Garg, Natasha Attard, Roberta J Caruana, Bhushan Dinkar Pawar, Sally I McClean, Sandra C Buttigieg, Neville Calleja

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Length of stay estimation; Admission rate characterization; Resource requirement forecasting; Patientcare modelling; Hospital capacity planning; Phase type survival trees; Machine Learning; Health ML Extended Health Intelligence

Online: 20 February 2023 (01:38:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints201806.0164.v1

Simple and Low-cost Model for Monthly and Yearly Streamflow Forecasts during the Current Hydrological Year

Fernando Delgado-Ramos, Carmen Hervás-Gámez

Subject: Engineering, Civil Engineering Keywords: Integrated water resources management; support to decision-making process, streamflow forecast; simple and low-cost forecasting model; Guadalquivir River Basin; Genil River; Canales reservoir; Quéntar reservoir

Online: 11 June 2018 (16:40:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.2119.v1

Renewable Energy Integration for Power Outage Mitigation: A Data-Driven Approach in Advancing Grid Resilience Strategies

Mahtab Murshed, Manohar Chamana, Konrad Schmitt, Suhas Pol, Olatunji Adeyanju, Stephen Bayne

Subject: Engineering, Electrical And Electronic Engineering Keywords: Grid resilience; Power outage prediction; Monte Carlo simulation; LSTM forecasting; Hybrid LSTM-PSO model; Battery State of Charge; Microgrid integration; Techno-economic analysis; Renewable energy; Energy independence

Online: 31 August 2023 (04:18:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202103.0412.v2

Fighting Deepfakes Using Body Language Analysis

Robail Yasrab, Wanqi Jiang, Adnan Riaz

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Imaging; Machine learning; Deepfakes; Human pose estimation; Upper body languages; World leader; Deep learning; Computer vision; Recurrent Neural Networks (RNNs); Long Short-term Memory(LSTM); machine learning; Forecasting

Online: 28 April 2021 (12:02:00 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202105.0116.v1

Forecasted Incidence, Intensive Care Unit Admissions and Projected Mortality Attributable to Covid-19 in Portugal, UK, Germany, Italy and France – 4 Weeks Ahead

Kathleen Carvalho, João Paulo Vicente, Mihajlo Jakovljevic, João Paulo Teixeira

Subject: Medicine And Pharmacology, Pulmonary And Respiratory Medicine Keywords: Time Series Prediction; ANN forecasting; New Coronavirus; COVID19 prediction cases; COVID19 prediction deaths; COVID19 prediction ICU, COVID19 Vaccination; COVID19 in Europe; COVID19 in Israel; COVID19 use of face mask.

Online: 6 May 2021 (16:58:01 CEST)

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Working Paper ARTICLE

Predicting Compression Index of Fine-Grained Soils Using Gene Expression Programming Model

Danial Mohammadzadeh, Seyed-Farzan Kazemi, Amir Mosavi, Ehsan Nasseralshariati

Subject: Engineering, Civil Engineering Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

Online: 25 March 2019 (10:25:18 CET)

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Working Paper ARTICLE

Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming

Danial Mohammadzadeh S., Seyed-Farzan Kazemi, Amir Mosavi, Ehsan Nasseralshariati

Subject: Engineering, Civil Engineering Keywords: Evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

Online: 25 March 2019 (10:21:45 CET)

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Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (C_c) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, C_c was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e₀). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate C_csuch as oedometer test. This study presents a novel prediction model for C_c of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C_c based on LL, PL, and e₀. The performance of developed GEP-based model was evaluated through coefficient of determination (R²), root mean squared error (RMSE), and mean average error (MAE). High R² and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R², RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining C_c of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

Preprint ARTICLE | doi:10.20944/preprints202401.0160.v1

Discrete Level DNI Classification for Enhanced Solar Energy Analysis

Franch Maverick Arellano Lorilla, Renyl Barroca

Subject: Engineering, Energy And Fuel Technology Keywords: solar irradiance nowcasting; deep learning in renewable energy; modular solar forecasting systems; computer vision for DNI estimation; ResNet-50 for energy prediction; cost-effective solar technology; high-resolution image processing; NVIDIA Jetson in solar energy; classification-based DNI analysis; sustainable energy optimization

Online: 3 January 2024 (08:36:12 CET)

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